mlsanityData to 14 Oct 2026

Inequity aversion improves cooperation in intertemporal social dilemmas

arXiv 1803.08884 ↗First posted 23 Mar 2018cs.NE cs.AI cs.GTcited by 106

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Abstract (arXiv)

Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas. Models based on behavioral economics are only able to explain this phenomenon for unrealistic stateless matrix games. Recently, multi-agent reinforcement learning has been applied to generalize social dilemma problems to temporally and spatially extended Markov games. However, this has not yet generated an agent that learns to cooperate in social dilemmas as humans do. A key insight is that many, but not all, human individuals have inequity averse social preferences. This promotes a particular resolution of the matrix game social dilemma wherein inequity-averse individuals are personally pro-social and punish defectors. Here we extend this idea to Markov games and show that it promotes cooperation in several types of sequential social dilemma, via a profitable interaction with policy learnability. In particular, we find that inequity aversion improves temporal credit assignment for the important class of intertemporal social dilemmas. These results help explain how large-scale cooperation may emerge and persist.

Key facts. Cited by 106 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Mar 2018.

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Citing papers per year: 2021: 23; 2022: 12; 2023: 20; 2024: 19; 2025: 11; 2026: 21.

106 papers.

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  1. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 181
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  3. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 99
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38 more
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  4. 2024Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmascited by 12
  5. 2025Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordiacited by 12
  6. 2024Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Gamescited by 11
  7. 2025MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learningcited by 11
  8. 2023Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learningcited by 10
  9. 2024Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contractscited by 9
  10. 2021Cooperative Multi-Agent Fairness and Equivariant Policiescited by 8
  11. 2025SocialJax: An Evaluation Suite for Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 8
  12. 2026M3-BENCH: Process-Aware Evaluation of LLM Agents' Social Behaviors in Mixed-Motive Gamescited by 8
  13. 2021Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RLcited by 7
  14. 2022Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibriacited by 7
  15. 2022The Importance of Credo in Multiagent Learningcited by 7
  16. 2023Hybrid Approaches for Moral Value Alignment in AI Agents: a Manifestocited by 7
  17. 2024Aligning Individual and Collective Objectives in Multi-Agent Cooperationcited by 7
  18. 2024Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planningcited by 7
  19. 2021A multi-agent reinforcement learning model of reputation and cooperation in human groupscited by 6
  20. 2023Meta-Value Learning: a General Framework for Learning with Learning Awarenesscited by 6
  21. 2024Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Gamescited by 6
  22. 2021Modelling Cooperation in Network Games with Spatio-Temporal Complexitycited by 5
  23. 2021Learning to Share in Multi-Agent Reinforcement Learningcited by 5
  24. 2022Exploring the Benefits of Teams in Multiagent Learningcited by 5
  25. 2023Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systemscited by 5
  26. 2023Mediated Multi-Agent Reinforcement Learningcited by 5
  27. 2021Statistical discrimination in learning agentscited by 4
  28. 2021Improved Cooperation by Exploiting a Common Signalcited by 3
  29. 2021Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environmentscited by 3
  30. 2021Improving Social Welfare While Preserving Autonomy via a Pareto Mediatorcited by 3
  31. 2021DAIR: Disentangled Attention Intrinsic Regularization for Safe and Efficient Bimanual Manipulationcited by 3
  32. 2023IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social Dilemmascited by 3
  33. 2024Reciprocal Reward Influence Encourages Cooperation From Self-Interested Agentscited by 3
  34. 2024Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learningcited by 3
  35. 2024Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactionscited by 3
  36. 2024InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemmacited by 3
  37. 2025Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesiscited by 3
  38. 2025Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcarecited by 3

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